• Journal of Convergence for Information Technology
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• v.7 no.6
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• pp.159-166
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• 2017

In this paper, we have experimented to improve the verification performance of speaker change detection on broadcast news. It is to enhance the input noisy speech and to apply the KL distance $D_s$ using the SNR-based weighting function $w_m$. The basic experimental system is the verification system of speaker change using GMM-UBM based KL distance D(Experiment 0). Experiment 1 applies the input noisy speech enhancement using MMSE Log-STSA. Experiment 2 applies the new KL distance $D_s$ to the system of Experiment 1. Experiments were conducted under the condition of 0% MDR in order to prevent missing information of speaker change. The FAR of Experiment 0 was 71.5%. The FAR of Experiment 1 was 67.3%, which was 4.2% higher than that of Experiment 0. The FAR of experiment 2 was 60.7%, which was 10.8% higher than that of experiment 0.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.5
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• pp.1-10
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• 2007

Most of natural images have a special property, what is called self-similarity, which is the basis of fractal image coding. Even though an image has local stationarity in several homogeneous regions, it is generally non-stationarysignal, especially in edge region. This is the main reason that poor results are induced in linear techniques. In order to overcome the difficulty we propose a non-linear technique using self-similarity in the image. In our work, an image is classified into stationary and non-stationary region with respect to sample variance. In case of stationary region, do-noising is performed as simply averaging of its neighborhoods. However, if the region is non-stationary region, stationalization is conducted as make a set of center pixels by similarity matching with respect to bMSE(block Mean Square Error). And then do-nosing is performed by Gaussian weighted averaging of center pixels of similar blocks, because the set of center pixels of similar blocks can be regarded as nearly stationary. The true image value is estimated by weighted average of the elements of the set. The experimental results show that our method has better performance and smaller variance than other methods as estimator.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.4
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• pp.554-561
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• 2013

In this paper, we introduce a modeling and a process simulator developed with the aid of pRBFNNs for activated sludge process in the sewage treatment system. Activated sludge process(ASP) of sewage treatment system facilities is a process that handles biological treatment reaction and is a very complex system with non-linear characteristics. In this paper, we carry out modeling by using essential ASP factors such as water effluent quality, the manipulated value of various pumps, and water inflow quality, and so on. Intelligent algorithms used for constructing process simulator are developed by considering multi-output polynomial radial basis function Neural Networks(pRBFNNs) as well as Fuzzy C-Means clustering and Particle Swarm Optimization. Here, the apexes of the antecedent gaussian functions of fuzzy rules are decided by C-means clustering algorithm and the apexes of the consequent part of fuzzy rules are learned by using back-propagation based on gradient decent method. Also, the parameters related to the fuzzy model are optimized by means of particle swarm optimization. The coefficients of the consequent polynomial of fuzzy rules and performance index are considered by the Least Square Estimation and Mean Squared Error. The descriptions of developed process simulator architecture and ensuing operation method are handled.

• 한국태양에너지학회:학술대회논문집
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• 2012.03a
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• pp.419-424
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• 2012

This paper discusses the state-of-the-art techniques in real-time state estimation for the Smart Microgrids. The most popular method used in traditional power system state estimation is a Weighted Least Square(WLS) algorithm which is based on Maximum Likelihood(ML) estimation under the assumption of static system state being a set of deterministic variables. In this paper, we present a survey of dynamic state estimation techniques for Smart Microgrids based on Belief Propagation (BP) when the system state is a set of stochastic variables. The measurements are often too sparse to fulfill the system observability in the distribution network of microgrids. The BP algorithm calculates posterior distributions of the state variables for real-time sparse measurements. Smart Microgrids are modeled as a factor graph suitable for characterizing the linear correlations among the state variables. The state estimator performs the BP algorithm on the factor graph based the stochastic model. The factor graph model can integrate new models for solar and wind correlation. It provides the Smart Microgrids with a way of integrating the distributed renewable energy generation. Our study on Smart Microgrid state estimation can be extended to the estimation of unbalanced three phase distribution systems as well as the optimal placement of smart meters.

• Journal of the Korean Society for Railway
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• v.7 no.2
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• pp.155-162
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• 2004

In order to account for a price variation of apartment that places near a newly constructed subway station, a spatial hedonic model was developed to examine spacial characteristics that affect a purchasing price of an apartment using a White Estimator. In particular, the paper aims to examine various effects of subway 7 construction on an apartment price in Seoul Metropolitan Area. As explanatory variables, an apartment size, distance to a closest subway station, distance to the Central Business District (CBD) of Seoul, the number of years after building, and a lagged variable of the apartment purchasing price were used. The lagged variable plays a role of representing a spatial weighted average of previous prices of other apartments that locate within 3 km from the apartment. For a precise study, an entire sample was divided into two sets, southern area and southwestern area of Seoul, and two different spatial hedonic models were estimated. Not only before and after analysis, but also with and without analysis were conducted to compare with different effects of the spatial characteristics of two areas. The results show that before the construction of the subway 7, the prices of the apartments in the southern area were more sensitive to the apartment size, the distance to a closest subway station, the distance to the CBD, and the prices of the other apartments locating within 3km rather than those in the southwestern area. After the construction, on contrast, it is found that the apartment purchasing prices in the southwestern area are more sensitive than those in the southern area due to people's expectation regarding a new development around the subway station. In addition, the prices of the apartments locating closely with a transfer station are more likely to go up by increase in the apartment size, the distance to the station, and the prices of the other apartments within 3 km. Compared with the negative effects of the distance to the station on the prices in the other models, the positive effect of the distance to the transfer station might be caused by the characteristics of commercial area in which people are not likely to live.

• Journal of the Korean Society for Nondestructive Testing
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• v.30 no.4
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• pp.302-310
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• 2010

In this paper, we studied Bagging neural network for predicting defect size of steam generator(SG) tube in nuclear power plant. Bagging is a method for creating an ensemble of estimator based on bootstrap sampling. For predicting defect size of SG tube, we first generated eddy current testing signals for 4 defect patterns of SG tube with various widths and depths. Then, we constructed single neural network(SNN) and Bagging neural network(BNN) to estimate width and depth of each defect. The estimation performance of SNN and BNN were measured by means of peak error. According to our experiment result, average peak error of SNN and BNN for estimating defect depth were 0.117 and 0.089mm, respectively. Also, in the case of estimating defect width, average peak error of SNN and BNN were 0.494 and 0.306mm, respectively. This shows that the estimation performance of BNN is superior to that of SNN.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.5C
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• pp.317-325
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• 2011

For OFDM (Orthogonal Frequency Division Multiplexing) systems, 2-D MMSE (2-Dimensional Minimum Mean Square Error) channel estimation provides optimal performance in frequency/time selective fading channel environment. However, the 2-D MMSE channel estimation has high computational complexity due to the large matrix size, because the 2-D MMSE channel estimation considers time as well as frequency axis for channel estimation. To reduce the computational complexity, we propose a modified 2-D MMSE channel estimator which is based on 1-D MMSE channel estimation with weighted sum. Furthermore, we consider RMS delay spread and Doppler frequency estimation for 2-D MMSE channel estimation. We show that the proposed method can significantly reduce computational complexity as well as that it can perform close to 2-D MMSE channel estimation.

• Journal of Korea Trade
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• v.26 no.7
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• pp.1-18
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• 2022

Purpose - Recent international studies have largely focused on measuring the welfare gains from increased trade varieties. To adequately capture the variety gains, it is of importance to estimate the elasticity of substitution between varieties of trade goods because it is one of the key parameters to determine the magnitude of the variety gains. Using the import data of South Korea, this paper shows that the elasticities vary substantially across the estimators, which affects the magnitude of the gains from trade. Design/methodology - Empirical studies working on the gains from trade variety have heavily depended on the estimation methods for the elasticity of substitution between trade varieties, developed by Feenstra (1994) and refined by Broda and Weinstein (2006). We estimate and compare the estimated elasticities for 8,945 HS 10 goods of South Korea, obtained from the three estimation methods: Feenstra's weighted least square (F-WLS), Feenstra's feasible generalized least square (F-FGLS), and Broda and Weinstein's feasible generalized least square (BW-FGLS). Findings - Using the estimated elasticities from the F-FGLS, considered as a suitable estimator, A typical Korean consumer saved 228 dollars per year by the greater access to new import varieties. This leads to gains from imported variety of 2.06% of GDP. In 2017, a typical Korean consumer would gain by 611 dollars, compared with 2000. China is the country with the largest contribution (28.4%), followed by Japan and USA. About 50% of all the welfare gains come from the imports from the three main trade partners. The Southern Asian countries are more important to the South Korean welfare gain than the Western European countries. Originality/value - Existing studies have chosen one of the methods without any criterion for the choice and then estimated the elasticities of substitution between varieties of trade goods. This paper focuses on the estimation specifications and methods as the cause of the disparity in estimated elasticities and welfare gains from trade variety. According to the Ramsey RESET and White tests, the F-FGLS estimates are relatively better compared to the F-WLS and BW-FGLS estimates. As another contribution, this paper provides the first measure of the welfare gains from trade variety for South Korea, using the estimated elasticities of substitution between trade varieties.